One of the goals of ubiquitous computing is to build applications that are sensitive to the computer user's context. One important aspect of context includes the people and places that are close to the user. For example, at conferences, meetings and other social engagements, people interact with one another fairly regularly, and often see the same people at many such events. Various mechanisms have been tried to help people remember and/or discover the identities of others.
These mechanisms are based on location sensing and/or proximity sensing. Location sensing determines the absolute position (e.g., x,y coordinates) of a person, whereas proximity sensing discovers other people around a given user. Note that one common way to determine proximity is to measure absolute locations of multiple people and other sensors, and use the absolute location data to compute distances, providing inferences about proximity; in other words, traditional location sensing systems require computing and comparing absolute locations, which can be then used to compute proximity by measuring everyone's (x,y) locations and simply computing the distance between them.
Various ways to automatically measure location include using Wi-Fi signal strengths, GPS, and active badges, often requiring the deployment of specialized hardware in the environment, e.g. satellites for GPS and special receivers and/or transmitters for active badges. However, measuring (x,y) locations is difficult, because it requires extensive setup and calibration; the problem is especially difficult indoors where many people spend most of their time, and where GPS does not work.
Examples of identity-discovery mechanisms based on location and/or proximity sensing include wireless “conference devices” that are aimed at assisting conference attendees with such information, generally comprising small wireless devices that can be easily carried or worn, normally by people in large groups, such as nTAG™, SpotMe, IntelliBadge™, and other wearable or digital assistant devices. Among the features of these devices are their awareness of location and/or who is nearby.
Other systems include Proxy Lady, a system for encouraging informal, spontaneous face-to-face meetings based on proximity, which is detected via personal digital assistants (PDAs) equipped with custom radio transceivers. Another system known as Trepia lets users communicate with other nearby users that it finds automatically; users can manually specify their location, and Trepia also uses wired and Wi-Fi network commonality to infer proximity. Another system known as iChat AV lets users on the same local network find each other for instant messaging or video conferencing. Similar systems for computer games let users on the same network find other nearby gamers. However, drawbacks to these mechanisms include that users have to be on the same network in order to find each other, and that only other people can be found, as opposed to other things.
In addition to discovering other people, many individuals would benefit from being able to discover other resources that are nearby. For example, a user of a mobile computing device may need to print a document, such as when out of the office, and would thus benefit from being able to detect the nearest available printer. There are well-established protocols for peer-to-peer device discovery using Bluetooth and Infrared Data Association (IrDA), however the discovery range of Bluetooth is limited to about 10 meters, and IrDA requires a clear line of sight between devices, and only works over a range of about one meter. Further, these mechanisms only find other properly-enabled computing devices, not other resources such as the nearest elevator, bathroom, or vending machine.
U.S. patent application Ser. No. 10/677,125, assigned to the assignee of the present invention and herein incorporated by reference, provides a mechanism whereby people can automatically discover who else is nearby, and also determine what other resources were near that person. To that end, wireless signal strengths (with respect to various base stations, or access points or the like) are gathered from participating resources such as network devices or previously calibrated locations, and then processed (e.g., by a server) to determine which devices are experiencing similar signal strengths. Those with similar signal strengths are determined to be in proximity to one another. With this information, information about another resource may be looked up or otherwise provided to a network device for presentation to a user of that device. While proximity-related mechanisms based on this technology work very well, improvements to this concept would be beneficial and advantageous.